Discrimination of cellulose fabrics using infrared spectroscopy and newly developed discriminant analysis

Indigo-dyed cellulose fibers and synthetic polymers in surface-feeding seabird chick regurgitates from the Gulf of Alaska

We provide evidence of anthropogenic materials ingestion in seabirds from a remote oceanic area, using regurgitates obtained from black-legged kittiwake (Rissa tridactyla) chicks from Middleton Island (Gulf of Alaska, USA). By means of GPS tracking of breeding adults, we identified foraging grounds where anthropogenic materials were most likely ingested. They were mainly located within the continental shelf of the Gulf of Alaska and near the Alaskan coastline. Anthropogenic cellulose fibers showed a high prevalence (85 % occurrence), whereas synthetic polymers (in the micro- and mesoplastics dimensional range) were less frequent (20 %). Most fibers (60 %) were blue and we confirmed the presence of indigo-dyed cellulosic fibers, characteristic of denim fabrics. In terms of mass, contamination levels were 0.077 μg g-1 wet weight and 0.009 μg g-1 wet weight for anthropogenic microfibers and synthetic polymers, respectively. These results represent the only recent report of contamination by anthropogenic fibers in seabirds from the Gulf of Alaska.

Non-destructive detection and classification of textile fibres based on hyperspectral imaging and 1D-CNN

Textile fibre is very common in daily life, and its classification and identification play an important role in textile recycling, archaeology, public security, and other industries. However, traditional identification methods are time-consuming, laborious, and often destructive to the samples. In order to quickly, accurately, and nondestructively classify and recognize textile fibres, this study established a textile fibre classification and recognition method based on hyperspectral imaging (HSI) and a one-dimensional convolutional neural network (1D-CNN) model. Hyperspectral images of 25 kinds of commercial textile fibres were collected and denoised by pixel fusion. Four traditional machine learning classification models, k-nearest neighbors (KNN), support vector machine (SVM), random forest (RF), and partial least squares-discriminant analysis (PLS-DA), were used to identify the data. The results show that RF has the highest classification accuracy, reaching 91.4%. Then a back propagation neural network (BPNN) model and a one-dimensional convolutional neural network (1D-CNN) model were constructed and compared with the traditional machine learning methods. The results show that the 1D-CNN models have 97.9% and 98.6% accuracy on the training and test sets, respectively. The precision (Pr), sensitivity (Se), specificity (Sp), and F1 score (F1 score) of the models reached 98.7%, 98.6%, 99.9%, and 98.6%, respectively, which were significantly better than the four traditional machine learning models. It seems that 1D-CNN combined with the HSI technique may be a potential method in the detection and classification of textile fibres.

Identification of cellulose textile fibers

Distinguishing different textile fibers is important for recycling waste textiles. Most studies on non-destructive optical textile identification have focused on classifying different synthetic and natural fibers but chemical recycling requires more detailed information on fiber composition and polymer properties. Here, we report the use of near infrared imaging spectroscopy and chemometrics for classifying natural and regenerated cellulose fibers. Our classifiers trained on images of consumer textiles showed 100% true positive rates based on model cross-validation and correctly identified on average 8-9 out of 10 test set pixels using images of specifically made cotton, viscose and lyocell samples of known compositions. These results are significant as they indicate the possibility to monitor and control fiber dosing and subsequent dope viscosity during chemical recycling of cellulose fibers. Our results also suggested the possibility to identify fibers purely based on polymer chain length. This finding opens the possibility to indirectly estimate dope viscosity and creates entirely new hypotheses for combining imaging spectroscopy with classification and regression methods within the broader field of cellulose modification.